Sketches Generated by VASkeGAN:
We allow the trained model to generate sketches after being conditioned by a sketch from a particular class. A sample of the generated images are shown in Figure REF . This confirms that the model is indeed generating meaningful sketches and not random strokes. Here too, it is very clear from Figure REF  that, the `scribble effect' of [1]} is alleviated by VASkeGAN. Following this, VASkeGAN is trained as a standalone GAN wherein the the encoder and the \(L_{KL}\)  are removed, retaining only the generator (decoder) and the discriminator with \(L_R\)  and adversarial loss. The sketches generated in this case for all the categories are just doodles without any discernible entity. This experimentally validates the fact that discrete outputs pose a difficulty for the gradient updates to be passed from discriminator to generator for the weight update. Hence empirically strengthening the argument in favour of the formulation of SkeGAN.
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